from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

Daal4py_kmeans_short: 0h 0m 1s

Daal4py_ridge: 0h 0m 2s

Kmeans_short: 0h 0m 3s

Daal4py_logisticregression: 0h 0m 4s

Daal4py_kmeans_tall: 0h 0m 8s

Ridge: 0h 0m 11s

Logisticregression: 0h 0m 21s

Kmeans_tall: 0h 0m 25s

Daal4py_kneighborsclassifier_kd_tree: 0h 0m 30s

Kneighborsclassifier_kd_tree: 0h 3m 1s

Daal4py_kneighborsclassifier: 0h 3m 3s

Catboost_symmetric: 0h 5m 9s

Xgboost: 0h 5m 10s

Catboost: 0h 5m 17s

Histgradientboostingclassifier: 0h 5m 31s

Lightgbm: 0h 6m 5s

Kneighborsclassifier: 0h 36m 7s

Total: 1h 11m 17s

print_env_info()

Benchmark environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config_file_path="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.150	0.000	5.338	0.000	-1	5	NaN	NaN	0.531	0.000	0.282	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	37.806	0.000	0.000	0.038	-1	5	0.825	0.923	2.228	0.033	16.971	0.254	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.018	0.000	0.183	-1	5	1.000	1.000	0.090	0.004	2.031	0.213	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.140	0.000	5.728	0.000	1	100	NaN	NaN	0.493	0.000	0.283	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.354	0.410	0.000	0.025	1	100	0.917	0.837	2.109	0.042	12.024	0.310	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.196	0.002	0.000	0.196	1	100	1.000	1.000	0.089	0.002	2.211	0.062	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.138	0.000	5.817	0.000	-1	100	NaN	NaN	0.502	0.000	0.274	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	37.176	0.000	0.000	0.037	-1	100	0.917	0.923	2.261	0.029	16.439	0.211	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.176	0.018	0.000	0.176	-1	100	1.000	1.000	0.088	0.002	1.993	0.213	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.135	0.000	5.946	0.000	1	1	NaN	NaN	0.514	0.000	0.262	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.655	0.142	0.000	0.014	1	1	0.734	0.745	2.223	0.029	6.141	0.103	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.190	0.003	0.000	0.190	1	1	0.000	0.000	0.089	0.001	2.136	0.045	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.135	0.000	5.941	0.000	-1	1	NaN	NaN	0.519	0.000	0.259	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.919	0.218	0.000	0.026	-1	1	0.734	0.745	2.213	0.034	11.715	0.207	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.172	0.020	0.000	0.172	-1	1	0.000	0.000	0.092	0.003	1.875	0.231	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.000	6.152	0.000	1	5	NaN	NaN	0.531	0.000	0.245	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.128	0.129	0.000	0.025	1	5	0.825	0.837	2.186	0.049	11.495	0.265	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.197	0.002	0.000	0.197	1	5	1.000	1.000	0.089	0.003	2.203	0.089	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.064	0.000	0.250	0.000	-1	5	NaN	NaN	0.117	0.000	0.547	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.932	0.000	0.000	0.034	-1	5	0.985	0.986	0.392	0.002	86.572	0.549	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.027	0.001	0.000	0.027	-1	5	1.000	1.000	0.007	0.001	3.914	0.380	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.076	0.000	0.212	0.000	1	100	NaN	NaN	0.115	0.000	0.659	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.343	0.104	0.000	0.021	1	100	0.984	0.986	0.341	0.015	62.629	2.708	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.001	0.000	0.020	1	100	1.000	1.000	0.007	0.001	2.924	0.318	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.062	0.000	0.257	0.000	-1	100	NaN	NaN	0.119	0.000	0.523	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.775	0.000	0.000	0.034	-1	100	0.984	0.986	0.411	0.011	82.267	2.170	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.004	0.000	0.028	-1	100	1.000	1.000	0.007	0.000	4.339	0.643	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.087	0.000	0.184	0.000	1	1	NaN	NaN	0.122	0.000	0.711	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	11.014	0.358	0.000	0.011	1	1	0.973	0.976	0.347	0.009	31.750	1.341	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.017	0.002	0.000	0.017	1	1	1.000	1.000	0.007	0.000	2.601	0.325	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.069	0.000	0.232	0.000	-1	1	NaN	NaN	0.120	0.000	0.574	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.321	0.403	0.000	0.023	-1	1	0.973	0.976	0.341	0.008	68.405	1.924	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.001	0.000	0.021	-1	1	1.000	1.000	0.007	0.001	3.151	0.519	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.067	0.000	0.238	0.000	1	5	NaN	NaN	0.121	0.000	0.556	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.389	0.065	0.000	0.021	1	5	0.985	0.986	0.339	0.004	63.008	0.856	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.001	0.000	0.021	1	5	1.000	1.000	0.007	0.001	2.934	0.462	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.495	0.000	0.023	0.000	-1	1	NaN	NaN	0.823	0.000	4.246	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.516	0.009	0.000	0.001	-1	1	0.943	0.982	0.198	0.006	2.601	0.085	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.002	0.000	0.004	-1	1	1.000	1.000	0.000	0.000	8.998	5.898	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.525	0.000	0.023	0.000	1	100	NaN	NaN	0.868	0.000	4.064	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	5.200	0.099	0.000	0.005	1	100	0.970	0.986	0.609	0.010	8.539	0.214	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	1	100	1.000	1.000	0.001	0.000	3.568	1.378	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.442	0.000	0.023	0.000	1	1	NaN	NaN	0.807	0.000	4.265	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.825	0.017	0.000	0.001	1	1	0.943	0.982	0.200	0.005	4.124	0.135	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.468	1.025	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.278	0.000	0.024	0.000	-1	100	NaN	NaN	0.825	0.000	3.975	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	3.170	0.158	0.000	0.003	-1	100	0.970	0.966	0.107	0.002	29.657	1.578	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.007	0.001	0.000	0.007	-1	100	1.000	1.000	0.000	0.000	22.150	10.362	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.432	0.000	0.023	0.000	-1	5	NaN	NaN	0.812	0.000	4.224	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.952	0.012	0.000	0.001	-1	5	0.971	0.986	0.586	0.008	1.624	0.029	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.002	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	3.828	2.144	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.546	0.000	0.023	0.000	1	5	NaN	NaN	0.784	0.000	4.523	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.635	0.021	0.000	0.002	1	5	0.971	0.966	0.105	0.002	15.549	0.391	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.000	0.000	5.198	2.777	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.944	0.000	0.017	0.000	-1	1	NaN	NaN	0.543	0.000	1.737	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.032	0.002	0.001	0.000	-1	1	0.973	0.980	0.001	0.000	24.996	8.644	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	17.745	14.001	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.935	0.000	0.017	0.000	1	100	NaN	NaN	0.543	0.000	1.722	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.065	0.004	0.000	0.000	1	100	0.984	0.983	0.008	0.001	7.770	1.398	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	4.696	2.799	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	1.020	0.000	0.016	0.000	1	1	NaN	NaN	0.562	0.000	1.815	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.030	0.004	0.001	0.000	1	1	0.973	0.980	0.002	0.000	19.780	6.863	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.370	3.658	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	1.001	0.000	0.016	0.000	-1	100	NaN	NaN	0.543	0.000	1.844	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.057	0.005	0.000	0.000	-1	100	0.984	0.969	0.001	0.000	55.074	17.298	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	16.398	13.094	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.973	0.000	0.016	0.000	-1	5	NaN	NaN	0.554	0.000	1.756	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.034	0.003	0.000	0.000	-1	5	0.981	0.983	0.009	0.002	3.874	0.765	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	22.193	14.213	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.968	0.000	0.017	0.000	1	5	NaN	NaN	0.550	0.000	1.760	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.034	0.002	0.000	0.000	1	5	0.981	0.969	0.001	0.000	37.333	14.751	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	6.362	5.422	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.756	0.000	0.635	0.000	random	NaN	30	NaN	0.484	0.0	1.564	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.268	0.000	random	0.001	30	0.001	0.000	0.0	11.007	6.302	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.001	0.000	0.002	random	1.000	30	1.000	0.000	0.0	13.238	9.468	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.706	0.000	0.680	0.000	k-means++	NaN	30	NaN	0.508	0.0	1.390	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.295	0.000	k-means++	0.001	30	0.001	0.000	0.0	10.174	6.027	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.002	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	16.916	16.508	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.971	0.000	3.443	0.000	random	NaN	30	NaN	2.968	0.0	2.349	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	10.292	0.000	random	0.002	30	0.002	0.000	0.0	7.839	4.502	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.001	0.013	0.002	random	1.000	30	1.000	0.000	0.0	11.953	7.729	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.735	0.000	3.563	0.000	k-means++	NaN	30	NaN	3.149	0.0	2.139	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	12.732	0.000	k-means++	0.002	30	0.002	0.000	0.0	6.440	2.764	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.016	0.002	k-means++	1.000	30	1.000	0.000	0.0	10.768	6.203	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.275	0.000	0.012	0.000	k-means++	NaN	20	NaN	0.109	0.0	2.531	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.151	0.000	k-means++	-0.000	20	-0.000	0.001	0.0	2.704	0.667	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.001	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	10.263	6.122	See	See
3	KMeans_short	fit	10000	10000	2	20	0.092	0.000	0.035	0.000	random	NaN	20	NaN	0.037	0.0	2.514	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.156	0.000	random	-0.001	20	0.000	0.001	0.0	2.721	0.410	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.000	0.000	0.002	random	1.000	20	1.000	0.000	0.0	9.876	5.447	See	See
6	KMeans_short	fit	10000	10000	100	20	0.724	0.000	0.221	0.000	k-means++	NaN	20	NaN	0.412	0.0	1.759	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.453	0.000	k-means++	0.278	20	0.319	0.001	0.0	2.053	0.344	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.574	4.113	See	See
9	KMeans_short	fit	10000	10000	100	20	0.253	0.000	0.633	0.000	random	NaN	20	NaN	0.166	0.0	1.524	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.313	0.000	random	0.320	20	0.373	0.001	0.0	2.101	0.442	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	random	1.000	20	1.000	0.000	0.0	7.657	4.446	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	12.228	0.0	[-0.09648522]	0.000	NaN	NaN	NaN	NaN	NaN	2.079	0.0	5.883	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.001	0.0	[31.6472972]	0.000	NaN	NaN	NaN	NaN	0.506	0.000	0.0	1.265	1.180	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.20765224]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.384	0.306	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.850	0.0	[2.44734793]	0.001	NaN	NaN	NaN	NaN	NaN	0.792	0.0	1.074	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[109.46005355]	0.000	NaN	NaN	NaN	NaN	0.240	0.003	0.0	0.562	0.081	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[19.65185975]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.136	0.092	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.201	0.000	0.399	0.0	NaN	NaN	NaN	0.200	0.0	1.005	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.001	7.660	0.0	NaN	NaN	0.094	0.017	0.0	0.619	0.036	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	1.062	0.0	NaN	NaN	NaN	0.000	0.0	0.658	0.636	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.610	0.000	0.497	0.0	NaN	NaN	NaN	0.266	0.0	6.046	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	5.214	0.0	NaN	NaN	1.000	0.000	0.0	0.601	0.391	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.012	0.0	NaN	NaN	NaN	0.000	0.0	0.621	0.600	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()

Raw results

Smoothed HPO Curves

25th percentile

Mean

75th percentile